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Fuzzy-Rough Nearest-Neighbor Classification Approach
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bian, Haiyun Mazlack, Lawrence J. |
| Copyright Year | 2003 |
| Abstract | This paper proposes a new --rough nearest-neighbor (NN ) approach based on the fuzzy-rough sets theory. This approach is more suitable to be used under partially exposed and unbalanced data set compared with crisp NN and fuzzy NN approach. Then the new method is applied to China listed company financial distress prediction, a typical classification task under partially exposed and unbalanced learning space. Results suggest that the compared with crisp and fuzzy nearest neighbor classification methods, this method provides more accurate prediction result under this research design. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://eecs.ceas.uc.edu/~mazlack/academic.UC/NAFIPS159.pdf |
| Alternate Webpage(s) | http://sci2s.ugr.es/keel/pdf/specific/congreso/01226836.pdf |
| Alternate Webpage(s) | http://www.ece.uc.edu/~mazlack/academic.UC/NAFIPS159.pdf |
| Alternate Webpage(s) | http://sci2s.ugr.es/keel/pdf/algorithm/congreso/2003-NAFIPS-Bian.pdf |
| Alternate Webpage(s) | http://secs.ceas.uc.edu/~mazlack/academic.UC/NAFIPS159.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Inventory K-nearest neighbors algorithm Nearest-neighbor interpolation Rough set Single Linkage Cluster Analysis Unbalanced circuit |
| Content Type | Text |
| Resource Type | Article |
